In recent years, adversarial learning becomes a new fashion in fair representation learning [1]}, [2]}, [3]}, [4]}.
For example, Zhang et al. zhang2018mitigating proposed to apply adversarial learning to the representations learned by a model by propagating the negative gradients of a sensitive attribute discriminator to the model.
They also proposed to remove the projection of task-specific gradients on the space of discriminator gradients to ensure that optimizing the loss in downstream tasks does not help the discriminator.
Madras et al. madras2018learning proposed to use the soft labels predicted by the adversary model and the hidden representations to reconstruct the input data.
Wu et al. wu2021fairness proposed a decomposed adversarial learning method that uses a bias-aware user model to capture bias information and uses a bias-free user model to capture bias-independent user interest.
The user embeddings learned by the two models are regularized to be orthogonal.
In these methods, an informative discriminator that can reflect the sensitive attribute space is a necessity for learning fair representations.
However, if only a limited amount of data has sensitive attribute labels, it is difficult to learn an accurate discriminator and thereby the attribute information cannot be effectively removed from hidden representations.
Moreover, existing semi-supervised fair representation learning methods mainly focus on the missing task labels while ignoring the scarcity of data with sensitive attribute labels [5]}, [6]}, [7]}.
Different from existing methods, our approach uses a semi-supervised variational autoencoder to exploit useful information from data without attribute labels, which can learn fair representations on limited samples with revealed sensitive attributes.
